90 citations · 314 across the 20 of their papers we have counts for
3 papers · 1 filter
Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy
Alex Berke, Ronan Doorley, Kent Larson +1
Location data collected from mobile devices represent mobility behaviors at individual and societal levels. These data have important applications ranging from transportation plann…
Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning
Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey +4
A common technique to improve learning performance in deep reinforcement learning (DRL) and many other machine learning algorithms is to run multiple learning agents in parallel. A…
How to Organize your Deep Reinforcement Learning Agents: The Importance of Communication Topology
Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey +3
In this empirical paper, we investigate how learning agents can be arranged in more efficient communication topologies for improved learning. This is an important problem because a…